A study on radar emitter recognition based on spds neural network

Information Technology Journal

Volumn 10, Issue 4, 2011, Pages 883-888

  Yin, Zhendong ^a   Yang, Wei ^b   Yang, Zhutian ^a   Zuo, Lihua ^c   Gao, Hansong ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b CENTER FOR EARTH OBSERVATION AND DIGITAL EARTH   (China)

^c CHINA ACADEMY OF SPACE TECHNOLOGY   (China)

Author keywords

Local minimum; Pulse description word; Recognition rate; Training time

Indexed keywords

BP ALGORITHM; BP NETWORKS; CLASSICAL NEURAL NETWORKS; LEARNING SPEED; LOCAL MINIMUMS; MODIFIED ALGORITHMS; ONE-DIMENSIONAL SEARCH; PROCESSING TIME; PULSE DESCRIPTION WORD; RADAR EMITTER SIGNALS; RADAR EMITTERS; RAPID DEVELOPMENT; RECOGNITION RATES; SIMULATION RESULT; SINGLE PARAMETER DYNAMIC SEARCHES; TRAINING TIME;

ALGORITHMS; INDUSTRIAL RESEARCH; NEURAL NETWORKS; OPTIMIZATION; RADAR; RADAR TRANSMITTERS; SIGNAL PROCESSING;

PARAMETER ESTIMATION;

EID: 79952234672     PISSN: 18125638     EISSN: 18125646     Source Type: Journal    
DOI: 10.3923/itj.2011.883.888     Document Type: Article

References (10)

1. Alsaade, F., 2010. A study of neural network and its properties of training and adaptability in enhancing accuracy in a multimodal biometrics scenario. Inform. Technol. J., 9: 188-191.
2. Feng, Y.J., S.Z. Zhang, X.F. Wang and J.H. Wen, 2000. Cause for paralysis ofBP network. J. Harbin Inst. Technol, 32: 81-83.
3. Guan, X., Y. He and X. Yi, 2005. Attribute measure recognition approach andits applications to emitter recognition. Sci. Inform. Sci. China Ser. F, 48:225-233. (Pubitemid 44113774)
4. Kou, H. and B.S. Wang, 2007. The RBF-PLS approach based on genetic algorithmand its application in radar model recognition. J. Electronics Inform. Technol,25: 1031-1034. (Pubitemid 46922309)
5. Li, G., W. Ya-Dong and S. Xiao-Hong, 2010. A privacy preserving neural networklearning algorithm for horizontally partitioned databases. Inform. Technol. J., 9:1-10.
6. Qiang-Zhu, M., 2006. The application of BP neural network in real estateinvestment risk analysis. Build. Sci. Res. Sichuan, 12: 52-56.
7. Wang X.F. and Y.J. Feng, 1997. A new learning algorithm for neural network. J.Harbin Inst. Technol, 29: 23-25.
8. Yang, H.C., 2010. Risk evaluation model on enterprises complex informationsystem: A study based on the BP neural network. J. Software, 5: 99-106.
9. Zhang, D.L., P. Quan, and Z. Hong-Cai, 2008. Modeling and simulation onradiator threatening evaluation based on degrees keeping close to idealpoint. J.Syst. Simul, 20: 3896-3898.
10. Zhang, Z.C., X. Guan and Y. He, 2009. Study on radar emitter recognitionsignal based on rough sets and RBF neural network. Proceedings of the EighthInternational Conference on Machine Learning and Cybernetics, July 12-15, Baoding,pp: 1225-1230.